用AI动态调节激光雷达定位精度，提升复杂环境下的导航可靠性。
Dynamic Recalibration in LiDAR SLAM: Integrating AI and Geometric Methods with Real-Time Feedback Using INAF Fusion
- 引入INAF模块，融合深度学习与几何里程计，实时调整注意力权重。
- 在KITTI数据集上验证，显著提升定位与三维建图的精度和适应性。
- 适合自动驾驶、机器人导航等需要高精度实时定位的场景。
本文提出一种新型激光雷达同时定位与地图构建（LiDAR SLAM）融合技术，旨在提升定位与三维建图精度。核心是提出的推断注意力融合（INAF）模块，将人工智能与几何里程计相结合。基于KITTI数据集的激光雷达数据，INAF模块根据环境反馈动态调整注意力权重，增强系统适应性与测量准确性。该方法有效提升了定位与3D建图的精度，展现了在复杂场景下增强自主导航系统的潜力。
原文摘要 · Abstract (English)
This paper presents a novel fusion technique for LiDAR Simultaneous Localization and Mapping (SLAM), aimed at improving localization and 3D mapping using LiDAR sensor. Our approach centers on the Inferred Attention Fusion (INAF) module, which integrates AI with geometric odometry. Utilizing the KITTI dataset's LiDAR data, INAF dynamically adjusts attention weights based on environmental feedback, enhancing the system's adaptability and measurement accuracy. This method advances the precision of both localization and 3D mapping, demonstrating the potential of our fusion technique to enhance autonomous navigation systems in complex scenarios.
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